



'''simple docstring'''






import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase:   Any					=		logging.get_logger(__name__)
def  lowerCamelCase__			(		_A					,     _A=False							):
  a		:   str	  =	OrderedDict()
  for key, value in state_dict.items():
    if encoder_only and not key.startswith('head'							):
      a		:   Dict	  =	'segformer.encoder.' + key
    if key.startswith('backbone'							):
      a		:   str	  =	key.replace('backbone'					,     'segformer.encoder'							)
    if "patch_embed" in key:
      # replace for example patch_embed1 by patch_embeddings.0
      a		:   Optional[int]	  =	key[key.find('patch_embed'							) + len('patch_embed'							)]
      a		:   List[Any]	  =	key.replace(f"""patch_embed{idx}"""					,     f"""patch_embeddings.{int(_A							)-1}"""							)
    if "norm" in key:
      a		:   List[Any]	  =	key.replace('norm'					,     'layer_norm'							)
    if "segformer.encoder.layer_norm" in key:
      # replace for example layer_norm1 by layer_norm.0
      a		:   Tuple	  =	key[key.find('segformer.encoder.layer_norm'							) + len('segformer.encoder.layer_norm'							)]
      a		:   int	  =	key.replace(f"""layer_norm{idx}"""					,     f"""layer_norm.{int(_A							)-1}"""							)
    if "layer_norm1" in key:
      a		:   Tuple	  =	key.replace('layer_norm1'					,     'layer_norm_1'							)
    if "layer_norm2" in key:
      a		:   int	  =	key.replace('layer_norm2'					,     'layer_norm_2'							)
    if "block" in key:
      # replace for example block1 by block.0
      a		:   Union[str, Any]	  =	key[key.find('block'							) + len('block'							)]
      a		:   Tuple	  =	key.replace(f"""block{idx}"""					,     f"""block.{int(_A							)-1}"""							)
    if "attn.q" in key:
      a		:   str	  =	key.replace('attn.q'					,     'attention.self.query'							)
    if "attn.proj" in key:
      a		:   int	  =	key.replace('attn.proj'					,     'attention.output.dense'							)
    if "attn" in key:
      a		:   Union[str, Any]	  =	key.replace('attn'					,     'attention.self'							)
    if "fc1" in key:
      a		:   List[Any]	  =	key.replace('fc1'					,     'dense1'							)
    if "fc2" in key:
      a		:   Union[str, Any]	  =	key.replace('fc2'					,     'dense2'							)
    if "linear_pred" in key:
      a		:   Optional[int]	  =	key.replace('linear_pred'					,     'classifier'							)
    if "linear_fuse" in key:
      a		:   str	  =	key.replace('linear_fuse.conv'					,     'linear_fuse'							)
      a		:   Optional[Any]	  =	key.replace('linear_fuse.bn'					,     'batch_norm'							)
    if "linear_c" in key:
      # replace for example linear_c4 by linear_c.3
      a		:   Tuple	  =	key[key.find('linear_c'							) + len('linear_c'							)]
      a		:   Optional[Any]	  =	key.replace(f"""linear_c{idx}"""					,     f"""linear_c.{int(_A							)-1}"""							)
    if key.startswith('head'							):
      a		:   str	  =	key.replace('head'					,     'classifier'							)
    a		:   Any	  =	value

  return new_state_dict
def  lowerCamelCase__			(		_A					,     _A							):
  # for each of the encoder blocks:
  for i in range(config.num_encoder_blocks							):
    for j in range(config.depths[i]							):
      # read in weights + bias of keys and values (which is a single matrix in the original implementation)
      a		:   Optional[int]	  =	state_dict.pop(f"""segformer.encoder.block.{i}.{j}.attention.self.kv.weight"""							)
      a		:   Tuple	  =	state_dict.pop(f"""segformer.encoder.block.{i}.{j}.attention.self.kv.bias"""							)
      # next, add keys and values (in that order) to the state dict
      a		:   Any	  =	kv_weight[
          : config.hidden_sizes[i], :
      ]
      a		:   Dict	  =	kv_bias[: config.hidden_sizes[i]]
      a		:   str	  =	kv_weight[
          config.hidden_sizes[i] :, :
      ]
      a		:   List[str]	  =	kv_bias[
          config.hidden_sizes[i] :
      ]
def  lowerCamelCase__			(		):
  a		:   Optional[Any]	  =	'http://images.cocodataset.org/val2017/000000039769.jpg'
  a		:   Optional[int]	  =	Image.open(requests.get(_A					,     stream=_A							).raw							)

  return image
@torch.no_grad()
def  lowerCamelCase__			(		_A					,     _A					,     _A							):
  a		:   int	  =	SegformerConfig()
  a		:   Any	  =	False

  # set attributes based on model_name
  a		:   Dict	  =	'huggingface/label-files'
  if "segformer" in model_name:
    a		:   Dict	  =	model_name[len('segformer.'							) : len('segformer.'							) + 2]
    if "ade" in model_name:
      a		:   List[Any]	  =	150
      a		:   Any	  =	'ade20k-id2label.json'
      a		:   Dict	  =	(1, 150, 128, 128)
    elif "city" in model_name:
      a		:   Any	  =	19
      a		:   Any	  =	'cityscapes-id2label.json'
      a		:   Union[str, Any]	  =	(1, 19, 128, 128)
    else:
      raise ValueError(f"""Model {model_name} not supported"""							)
  elif "mit" in model_name:
    a		:   Any	  =	True
    a		:   Dict	  =	model_name[4:6]
    a		:   Tuple	  =	1000
    a		:   int	  =	'imagenet-1k-id2label.json'
    a		:   Any	  =	(1, 1000)
  else:
    raise ValueError(f"""Model {model_name} not supported"""							)

  # set config attributes
  a		:   str	  =	json.load(open(hf_hub_download(_A					,     _A					,     repo_type='dataset'							)					,     'r'							)							)
  a		:   List[str]	  =	{int(_A							): v for k, v in idalabel.items()}
  a		:   Tuple	  =	idalabel
  a		:   Any	  =	{v: k for k, v in idalabel.items()}
  if size == "b0":
    pass
  elif size == "b1":
    a		:   Dict	  =	[64, 128, 320, 512]
    a		:   str	  =	256
  elif size == "b2":
    a		:   str	  =	[64, 128, 320, 512]
    a		:   int	  =	768
    a		:   str	  =	[3, 4, 6, 3]
  elif size == "b3":
    a		:   Dict	  =	[64, 128, 320, 512]
    a		:   int	  =	768
    a		:   List[str]	  =	[3, 4, 18, 3]
  elif size == "b4":
    a		:   str	  =	[64, 128, 320, 512]
    a		:   Optional[int]	  =	768
    a		:   List[Any]	  =	[3, 8, 27, 3]
  elif size == "b5":
    a		:   int	  =	[64, 128, 320, 512]
    a		:   Union[str, Any]	  =	768
    a		:   List[str]	  =	[3, 6, 40, 3]
  else:
    raise ValueError(f"""Size {size} not supported"""							)

  # load image processor (only resize + normalize)
  a		:   Any	  =	SegformerImageProcessor(
      image_scale=(512, 512)					,     keep_ratio=_A					,     align=_A					,     do_random_crop=_A							)

  # prepare image
  a		:   Union[str, Any]	  =	prepare_img()
  a		:   Optional[int]	  =	image_processor(images=_A					,     return_tensors='pt'							).pixel_values

  logger.info(f"""Converting model {model_name}..."""							)

  # load original state dict
  if encoder_only:
    a		:   Dict	  =	torch.load(_A					,     map_location=torch.device('cpu'							)							)
  else:
    a		:   Optional[int]	  =	torch.load(_A					,     map_location=torch.device('cpu'							)							)['state_dict']

  # rename keys
  a		:   int	  =	rename_keys(_A					,     encoder_only=_A							)
  if not encoder_only:
    del state_dict["decode_head.conv_seg.weight"]
    del state_dict["decode_head.conv_seg.bias"]

  # key and value matrices need special treatment
  read_in_k_v(_A					,     _A							)

  # create HuggingFace model and load state dict
  if encoder_only:
    a		:   Optional[int]	  =	False
    a		:   Optional[Any]	  =	SegformerForImageClassification(_A							)
  else:
    a		:   Union[str, Any]	  =	SegformerForSemanticSegmentation(_A							)
  model.load_state_dict(_A							)
  model.eval()

  # forward pass
  a		:   List[str]	  =	model(_A							)
  a		:   Union[str, Any]	  =	outputs.logits

  # set expected_slice based on model name
  # ADE20k checkpoints
  if model_name == "segformer.b0.512x512.ade.160k":
    a		:   Optional[int]	  =	torch.tensor(
        [
            [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
            [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
            [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
        ]							)
  elif model_name == "segformer.b1.512x512.ade.160k":
    a		:   Optional[Any]	  =	torch.tensor(
        [
            [[-7.5820, -8.7231, -8.3215], [-8.0600, -10.3529, -10.0304], [-7.5208, -9.4103, -9.6239]],
            [[-12.6918, -13.8994, -13.7137], [-13.3196, -15.7523, -15.4789], [-12.9343, -14.8757, -14.9689]],
            [[-11.1911, -11.9421, -11.3243], [-11.3342, -13.6839, -13.3581], [-10.3909, -12.1832, -12.4858]],
        ]							)
  elif model_name == "segformer.b2.512x512.ade.160k":
    a		:   Union[str, Any]	  =	torch.tensor(
        [
            [[-11.8173, -14.3850, -16.3128], [-14.5648, -16.5804, -18.6568], [-14.7223, -15.7387, -18.4218]],
            [[-15.7290, -17.9171, -19.4423], [-18.3105, -19.9448, -21.4661], [-17.9296, -18.6497, -20.7910]],
            [[-15.0783, -17.0336, -18.2789], [-16.8771, -18.6870, -20.1612], [-16.2454, -17.1426, -19.5055]],
        ]							)
  elif model_name == "segformer.b3.512x512.ade.160k":
    a		:   Any	  =	torch.tensor(
        [
            [[-9.0878, -10.2081, -10.1891], [-9.3144, -10.7941, -10.9843], [-9.2294, -10.3855, -10.5704]],
            [[-12.2316, -13.9068, -13.6102], [-12.9161, -14.3702, -14.3235], [-12.5233, -13.7174, -13.7932]],
            [[-14.6275, -15.2490, -14.9727], [-14.3400, -15.9687, -16.2827], [-14.1484, -15.4033, -15.8937]],
        ]							)
  elif model_name == "segformer.b4.512x512.ade.160k":
    a		:   Union[str, Any]	  =	torch.tensor(
        [
            [[-12.3144, -13.2447, -14.0802], [-13.3614, -14.5816, -15.6117], [-13.3340, -14.4433, -16.2219]],
            [[-19.2781, -20.4128, -20.7506], [-20.6153, -21.6566, -22.0998], [-19.9800, -21.0430, -22.1494]],
            [[-18.8739, -19.7804, -21.1834], [-20.1233, -21.6765, -23.2944], [-20.0315, -21.2641, -23.6944]],
        ]							)
  elif model_name == "segformer.b5.640x640.ade.160k":
    a		:   Dict	  =	torch.tensor(
        [
            [[-9.5524, -12.0835, -11.7348], [-10.5229, -13.6446, -14.5662], [-9.5842, -12.8851, -13.9414]],
            [[-15.3432, -17.5323, -17.0818], [-16.3330, -18.9255, -19.2101], [-15.1340, -17.7848, -18.3971]],
            [[-12.6072, -14.9486, -14.6631], [-13.7629, -17.0907, -17.7745], [-12.7899, -16.1695, -17.1671]],
        ]							)
  # Cityscapes checkpoints
  elif model_name == "segformer.b0.1024x1024.city.160k":
    a		:   Tuple	  =	torch.tensor(
        [
            [[-11.9295, -13.4057, -14.8106], [-13.3431, -14.8179, -15.3781], [-14.2836, -15.5942, -16.1588]],
            [[-11.4906, -12.8067, -13.6564], [-13.1189, -14.0500, -14.1543], [-13.8748, -14.5136, -14.8789]],
            [[0.5374, 0.1067, -0.4742], [0.1141, -0.2255, -0.7099], [-0.3000, -0.5924, -1.3105]],
        ]							)
  elif model_name == "segformer.b0.512x1024.city.160k":
    a		:   Dict	  =	torch.tensor(
        [
            [[-7.8217, -9.8767, -10.1717], [-9.4438, -10.9058, -11.4047], [-9.7939, -12.3495, -12.1079]],
            [[-7.1514, -9.5336, -10.0860], [-9.7776, -11.6822, -11.8439], [-10.1411, -12.7655, -12.8972]],
            [[0.3021, 0.0805, -0.2310], [-0.0328, -0.1605, -0.2714], [-0.1408, -0.5477, -0.6976]],
        ]							)
  elif model_name == "segformer.b0.640x1280.city.160k":
    a		:   str	  =	torch.tensor(
        [
            [
                [-1.1372E01, -1.2787E01, -1.3477E01],
                [-1.2536E01, -1.4194E01, -1.4409E01],
                [-1.3217E01, -1.4888E01, -1.5327E01],
            ],
            [
                [-1.4791E01, -1.7122E01, -1.8277E01],
                [-1.7163E01, -1.9192E01, -1.9533E01],
                [-1.7897E01, -1.9991E01, -2.0315E01],
            ],
            [
                [7.6723E-01, 4.1921E-01, -7.7878E-02],
                [4.7772E-01, 9.5557E-03, -2.8082E-01],
                [3.6032E-01, -2.4826E-01, -5.1168E-01],
            ],
        ]							)
  elif model_name == "segformer.b0.768x768.city.160k":
    a		:   Union[str, Any]	  =	torch.tensor(
        [
            [[-9.4959, -11.3087, -11.7479], [-11.0025, -12.6540, -12.3319], [-11.4064, -13.0487, -12.9905]],
            [[-9.8905, -11.3084, -12.0854], [-11.1726, -12.7698, -12.9583], [-11.5985, -13.3278, -14.1774]],
            [[0.2213, 0.0192, -0.2466], [-0.1731, -0.4213, -0.4874], [-0.3126, -0.6541, -1.1389]],
        ]							)
  elif model_name == "segformer.b1.1024x1024.city.160k":
    a		:   str	  =	torch.tensor(
        [
            [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
            [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
            [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
        ]							)
  elif model_name == "segformer.b2.1024x1024.city.160k":
    a		:   Any	  =	torch.tensor(
        [
            [[-16.0976, -16.4856, -17.3962], [-16.6234, -19.0342, -19.7685], [-16.0900, -18.0661, -19.1180]],
            [[-18.4750, -18.8488, -19.5074], [-19.4030, -22.1570, -22.5977], [-19.1191, -20.8486, -22.3783]],
            [[-4.5178, -5.5037, -6.5109], [-5.0884, -7.2174, -8.0334], [-4.4156, -5.8117, -7.2970]],
        ]							)
  elif model_name == "segformer.b3.1024x1024.city.160k":
    a		:   List[Any]	  =	torch.tensor(
        [
            [[-14.2081, -14.4732, -14.1977], [-14.5867, -16.4423, -16.6356], [-13.4441, -14.9685, -16.8696]],
            [[-14.4576, -14.7073, -15.0451], [-15.0816, -17.6237, -17.9873], [-14.4213, -16.0199, -18.5992]],
            [[-4.7349, -4.9588, -5.0966], [-4.3210, -6.9325, -7.2591], [-3.4312, -4.7484, -7.1917]],
        ]							)
  elif model_name == "segformer.b4.1024x1024.city.160k":
    a		:   Optional[Any]	  =	torch.tensor(
        [
            [[-11.7737, -11.9526, -11.3273], [-13.6692, -14.4574, -13.8878], [-13.8937, -14.6924, -15.9345]],
            [[-14.6706, -14.5330, -14.1306], [-16.1502, -16.8180, -16.4269], [-16.8338, -17.8939, -20.1746]],
            [[1.0491, 0.8289, 1.0310], [1.1044, 0.5219, 0.8055], [1.0899, 0.6926, 0.5590]],
        ]							)
  elif model_name == "segformer.b5.1024x1024.city.160k":
    a		:   Dict	  =	torch.tensor(
        [
            [[-12.5641, -13.4777, -13.0684], [-13.9587, -15.8983, -16.6557], [-13.3109, -15.7350, -16.3141]],
            [[-14.7074, -15.4352, -14.5944], [-16.6353, -18.1663, -18.6120], [-15.1702, -18.0329, -18.1547]],
            [[-1.7990, -2.0951, -1.7784], [-2.6397, -3.8245, -3.9686], [-1.5264, -2.8126, -2.9316]],
        ]							)
  else:
    a		:   Optional[int]	  =	logits.argmax(-1							).item()
    print('Predicted class:'					,     model.config.idalabel[predicted_class_idx]							)

  # verify logits
  if not encoder_only:
    assert logits.shape == expected_shape
    assert torch.allclose(logits[0, :3, :3, :3]					,     _A					,     atol=1E-2							)

  # finally, save model and image processor
  logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""							)
  Path(_A							).mkdir(exist_ok=_A							)
  model.save_pretrained(_A							)
  image_processor.save_pretrained(_A							)


if __name__ == "__main__":
     lowerCAmelCase:   Optional[Any]					=		argparse.ArgumentParser()

     parser.add_argument(
         '--model_name',
         default='segformer.b0.512x512.ade.160k',
         type=str,
         help='Name of the model you\'d like to convert.',
     )
     parser.add_argument(
         '--checkpoint_path', default=None, type=str, help='Path to the original PyTorch checkpoint (.pth file).'
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
     )
     lowerCAmelCase:   Any					=		parser.parse_args()
     convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)




'''simple docstring'''






import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
lowerCAmelCase:   Dict					=		'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase:   Union[str, Any]					=		direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
lowerCAmelCase:   Union[str, Any]					=		re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
lowerCAmelCase:   Optional[Any]					=		re.compile(r'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
lowerCAmelCase:   int					=		re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowerCAmelCase:   List[Any]					=		[
    ('pretraining', 'MODEL_FOR_PRETRAINING_MAPPING_NAMES', 'AutoModelForPreTraining'),
    ('feature-extraction', 'MODEL_MAPPING_NAMES', 'AutoModel'),
    ('audio-classification', 'MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForAudioClassification'),
    ('text-generation', 'MODEL_FOR_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForCausalLM'),
    ('automatic-speech-recognition', 'MODEL_FOR_CTC_MAPPING_NAMES', 'AutoModelForCTC'),
    ('image-classification', 'MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForImageClassification'),
    ('image-segmentation', 'MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES', 'AutoModelForImageSegmentation'),
    ('fill-mask', 'MODEL_FOR_MASKED_LM_MAPPING_NAMES', 'AutoModelForMaskedLM'),
    ('object-detection', 'MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES', 'AutoModelForObjectDetection'),
    (
        'zero-shot-object-detection',
        'MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES',
        'AutoModelForZeroShotObjectDetection',
    ),
    ('question-answering', 'MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES', 'AutoModelForQuestionAnswering'),
    ('text2text-generation', 'MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForSeq2SeqLM'),
    ('text-classification', 'MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForSequenceClassification'),
    ('automatic-speech-recognition', 'MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES', 'AutoModelForSpeechSeq2Seq'),
    (
        'table-question-answering',
        'MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES',
        'AutoModelForTableQuestionAnswering',
    ),
    ('token-classification', 'MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForTokenClassification'),
    ('multiple-choice', 'MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES', 'AutoModelForMultipleChoice'),
    (
        'next-sentence-prediction',
        'MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES',
        'AutoModelForNextSentencePrediction',
    ),
    (
        'audio-frame-classification',
        'MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES',
        'AutoModelForAudioFrameClassification',
    ),
    ('audio-xvector', 'MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES', 'AutoModelForAudioXVector'),
    (
        'document-question-answering',
        'MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES',
        'AutoModelForDocumentQuestionAnswering',
    ),
    (
        'visual-question-answering',
        'MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES',
        'AutoModelForVisualQuestionAnswering',
    ),
    ('image-to-text', 'MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES', 'AutoModelForVision2Seq'),
    (
        'zero-shot-image-classification',
        'MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES',
        'AutoModelForZeroShotImageClassification',
    ),
    ('depth-estimation', 'MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES', 'AutoModelForDepthEstimation'),
    ('video-classification', 'MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForVideoClassification'),
    ('mask-generation', 'MODEL_FOR_MASK_GENERATION_MAPPING_NAMES', 'AutoModelForMaskGeneration'),
]
def  lowerCamelCase__			(		_A							):
  a		:   Optional[Any]	  =	re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'					,     _A							)
  return [m.group(0							) for m in matches]
def  lowerCamelCase__			(		):
  a		:   Union[str, Any]	  =	transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
  a		:   Union[str, Any]	  =	{
      config.replace('Config'					,     ''							): model_type for model_type, config in config_maping_names.items()
  }

  # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
  a		:   Union[str, Any]	  =	collections.defaultdict(_A							)
  a		:   Optional[Any]	  =	collections.defaultdict(_A							)
  a		:   int	  =	collections.defaultdict(_A							)

  # Let's lookup through all transformers object (once) and find if models are supported by a given backend.
  for attr_name in dir(_A							):
    a		:   Optional[Any]	  =	None
    if _re_tf_models.match(_A							) is not None:
      a		:   List[str]	  =	tf_models
      a		:   str	  =	_re_tf_models.match(_A							).groups()[0]
    elif _re_flax_models.match(_A							) is not None:
      a		:   Optional[int]	  =	flax_models
      a		:   List[Any]	  =	_re_flax_models.match(_A							).groups()[0]
    elif _re_pt_models.match(_A							) is not None:
      a		:   int	  =	pt_models
      a		:   List[str]	  =	_re_pt_models.match(_A							).groups()[0]

    if lookup_dict is not None:
      while len(_A							) > 0:
        if attr_name in model_prefix_to_model_type:
          a		:   Optional[Any]	  =	True
          break
        # Try again after removing the last word in the name
        a		:   List[str]	  =	''.join(camel_case_split(_A							)[:-1]							)

  a		:   Any	  =	set(list(pt_models.keys()							) + list(tf_models.keys()							) + list(flax_models.keys()							)							)
  a		:   int	  =	list(_A							)
  all_models.sort()

  a		:   Optional[Any]	  =	{'model_type': all_models}
  a		:   List[str]	  =	[pt_models[t] for t in all_models]
  a		:   str	  =	[tf_models[t] for t in all_models]
  a		:   int	  =	[flax_models[t] for t in all_models]

  # Now let's use the auto-mapping names to make sure
  a		:   Any	  =	{}
  for t in all_models:
    if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
      a		:   Optional[int]	  =	'AutoProcessor'
    elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
      a		:   Union[str, Any]	  =	'AutoTokenizer'
    elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
      a		:   Tuple	  =	'AutoFeatureExtractor'
    else:
      # Default to AutoTokenizer if a model has nothing, for backward compatibility.
      a		:   str	  =	'AutoTokenizer'

  a		:   str	  =	[processors[t] for t in all_models]

  return pd.DataFrame(_A							)
def  lowerCamelCase__			(		_A							):
  a		:   List[Any]	  =	[
      transformers_module.models.auto.modeling_auto,
      transformers_module.models.auto.modeling_tf_auto,
      transformers_module.models.auto.modeling_flax_auto,
  ]
  for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
    a		:   Optional[Any]	  =	[model_mapping, f"""TF_{model_mapping}""", f"""FLAX_{model_mapping}"""]
    a		:   Optional[int]	  =	[auto_class, f"""TF_{auto_class}""", f"""Flax_{auto_class}"""]
    # Loop through all three frameworks
    for module, cls, mapping in zip(_A					,     _A					,     _A							):
      # The type of pipeline may not exist in this framework
      if not hasattr(_A					,     _A							):
        continue
      # First extract all model_names
      a		:   List[str]	  =	[]
      for name in getattr(_A					,     _A							).values():
        if isinstance(_A					,     _A							):
          model_names.append(_A							)
        else:
          model_names.extend(list(_A							)							)

            # Add pipeline tag and auto model class for those models
      table.update({model_name: (pipeline_tag, cls) for model_name in model_names}							)

  return table
def  lowerCamelCase__			(		_A					,     _A							):
  a		:   Optional[int]	  =	get_frameworks_table()
  a		:   List[str]	  =	Dataset.from_pandas(_A							)

  a		:   Optional[Any]	  =	hf_hub_download(
      'huggingface/transformers-metadata'					,     'pipeline_tags.json'					,     repo_type='dataset'					,     token=_A							)
  a		:   List[Any]	  =	Dataset.from_json(_A							)
  a		:   Optional[Any]	  =	{
      tags_dataset[i]['model_class']: (tags_dataset[i]['pipeline_tag'], tags_dataset[i]['auto_class'])
      for i in range(len(_A							)							)
  }
  a		:   Dict	  =	update_pipeline_and_auto_class_table(_A							)

  # Sort the model classes to avoid some nondeterministic updates to create false update commits.
  a		:   str	  =	sorted(table.keys()							)
  a		:   List[Any]	  =	pd.DataFrame(
      {
          'model_class': model_classes,
          'pipeline_tag': [table[m][0] for m in model_classes],
          'auto_class': [table[m][1] for m in model_classes],
      }							)
  a		:   Optional[Any]	  =	Dataset.from_pandas(_A							)

  with tempfile.TemporaryDirectory() as tmp_dir:
    frameworks_dataset.to_json(os.path.join(_A					,     'frameworks.json'							)							)
    tags_dataset.to_json(os.path.join(_A					,     'pipeline_tags.json'							)							)

    if commit_sha is not None:
      a		:   int	  =	(
          f"""Update with commit {commit_sha}\n\nSee: """
          f"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
      )
    else:
      a		:   Optional[int]	  =	'Update'

    upload_folder(
        repo_id='huggingface/transformers-metadata'					,     folder_path=_A					,     repo_type='dataset'					,     token=_A					,     commit_message=_A					,     )
def  lowerCamelCase__			(		):
  a		:   int	  =	{tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
  a		:   List[Any]	  =	transformers_module.pipelines.SUPPORTED_TASKS
  a		:   str	  =	[]
  for key in pipeline_tasks:
    if key not in in_table:
      a		:   Dict	  =	pipeline_tasks[key]['pt']
      if isinstance(_A					,     (list, tuple)							):
        a		:   List[Any]	  =	model[0]
      a		:   Union[str, Any]	  =	model.__name__
      if model not in in_table.values():
        missing.append(_A							)

  if len(_A							) > 0:
    a		:   List[str]	  =	', '.join(_A							)
    raise ValueError(
        'The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside '
        f"""`utils/update_metadata.py`: {msg}. Please add them!"""							)


if __name__ == "__main__":
     lowerCAmelCase:   Tuple					=		argparse.ArgumentParser()
     parser.add_argument('--token', type=str, help='The token to use to push to the transformers-metadata dataset.')
     parser.add_argument('--commit_sha', type=str, help='The sha of the commit going with this update.')
     parser.add_argument('--check-only', action='store_true', help='Activate to just check all pipelines are present.')
     lowerCAmelCase:   Optional[Any]					=		parser.parse_args()

     if args.check_only:
          check_pipeline_tags()
     else:
          update_metadata(args.token, args.commit_sha)